Instructions to use HooshvareLab/bert-fa-zwnj-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-fa-zwnj-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HooshvareLab/bert-fa-zwnj-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-zwnj-base") model = AutoModelForMaskedLM.from_pretrained("HooshvareLab/bert-fa-zwnj-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 835d8b20a0787ffdac2231bd2b2c87d59af19a12845ff3db965f169ef73ad50f
- Size of remote file:
- 473 MB
- SHA256:
- 20a11f8b0dde90ec76b142f05b449379104e9f902ec54b44a332cf6a947ea474
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.